S000424 Visualise quality control
Daniel Brown
2024-11-21
S000424 TurboCapture-Seq compare lysis buffers
Recap
Checked 16 well barcoded second strand primers. Mapping rates and background looks good. With a view to future time course experiments with suspension cells, check the compatibility of various lysis buffers.
Aim
Test a variety of lysis buffers for compatibility with TurboCaptureSeq
Samples
- Rep 1 (columns 1 and 2)
- Rep 2 (columns 4 and 5)
- Rep 3 (columns 7 and 8)
- Rep 4 (columns 10 and 11)
Lab notes
SUPERase-In RNase inhibitor was used instead of RNaseOUT.
After reverse transcription, washed twice with 180 µL of 10 mM Tris before removing all remaining liquid.
Piper only used 8 well barcoded primers instead of the intended 16, so instead of pairs of columns as a replicate for a total of 4 replicates, now have individual columns as replicates for a total of 8 replicates:
- Rep 1 = Column 1 = Rep1A
- Rep 2 = Column 2 = Rep1B
- Rep 3 = Column 4 = Rep2A
- Rep 4 = Column 5 = Rep2B
- Rep 5 = Column 7 = Rep3A
- Rep 6 = Column 8 = Rep3B
- Rep 7 = Column 10 = Rep4A
- Rep 8 = Column 11 = Rep4B
Stopped after 0.8x SPRI cleanup and eluted in 20 µL.
Following PCR 2, performed the 2 sided selection.
Performed dsDNA HS Qubit for both PCR1 and PCR2 samples using 2 ÎĽL of sample as per standard protocol.
Replicates 2, 3, 5, 6, 7, 8 were diluted to be between1 - 5 ng/ÎĽL for the D5000 Tape.
Notebook recap
SCE object in generate in 1A_generateSCE_reads notebook.
Notebook aim
Plot the QC metrics.
Check on plate map representation
Plot on plate map
Dot plots by lysis buffer
Something went wrong with replicate 2B but otherwise looks reasonable. Not much difference between
- 1x TCL = cells washed in PBS and pellet resuspended in lysis buffer
- 2x TCL = cells in media where 2 x TCL is added in equal vol to cells
- DRS = DNA RNA shield didn’t really work
Library size log
plt1 <- ggplot(tb,
aes(x = Buffer, y= sum, colour = Sample)) +
geom_point() +
ylab("Library Size (UMIs)") +
xlab("Lysis buffer") +
scale_y_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "l") +
scale_colour_Publication()
plt1
Library size in deduplicated UMIs
Library size natural scale
plt2 <- ggplot(tb,
aes(x = Buffer, y= sum, colour = Sample, label = Replicate)) +
geom_text() +
ylab("Library Size (UMIs)") +
xlab("Lysis buffer") +
scale_fill_brewer(palette = "Dark2")
plt2
Library size in deduplicated UMIs
Detected genes
plt3 <- ggplot(tb,
aes(x = Buffer, y= detected, colour = Sample, label=Replicate)) +
geom_text() +
ylab("Genes detected") +
xlab("Lysis buffer") +
scale_fill_brewer(palette = "Dark2")
plt3
Number of detected genes
Genes v UMIs
plt4 <- ggplot(tb,
aes(x = detected+1, y= sum+1, colour = Buffer, label=Replicate)) +
geom_text() +
ylab("Library size (UMIs)") +
xlab("Genes detected") +
scale_y_continuous(trans='log10') + scale_x_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "bl") +
scale_colour_brewer(palette = "Dark2")
plt4
UMIs versus genes
Check UMIs are not saturated
I ordered an inital 10nt UMI sequence in the TSO, 1,048,576 molecules. Check this is not saturated.
human <- scater::plotHighestExprs(altExp(sce, "Human"), n=10, colour_cells_by = "Sample") + theme_Publication()
human
## AATGTATCAAAGTGTGTG AATGTATCCAGAGTCCTT AATGTATCCATTTGCGAT AATGTATCCCACGTCTAC
## 321 61516 329 34676
## AATGTATCGAGTTATGTG AATGTATCGCAACGACTC AATGTATCGCATTGATCT AATGTATCTGCACAGAGA
## 19521 1098 541 38545
## CCGGCAACAAAGTGTGTG CCGGCAACCAGAGTCCTT CCGGCAACCATTTGCGAT CCGGCAACCCACGTCTAC
## 509 52897 184 31047
## CCGGCAACGAGTTATGTG CCGGCAACGCAACGACTC CCGGCAACGCATTGATCT CCGGCAACTGCACAGAGA
## 18829 1716 729 33804
## CGGCTGGAAAAGTGTGTG CGGCTGGACAGAGTCCTT CGGCTGGACATTTGCGAT CGGCTGGACCACGTCTAC
## 45 28054 59 9244
## CGGCTGGAGAGTTATGTG CGGCTGGAGCAACGACTC CGGCTGGAGCATTGATCT CGGCTGGATGCACAGAGA
## 6742 176 143 11567
## CTCCTTTAGAGTTATGTG CTCCTTTATGCACAGAGA GAGACGCAAAAGTGTGTG GAGACGCACAGAGTCCTT
## 0 0 166 46230
## GAGACGCACATTTGCGAT GAGACGCACCACGTCTAC GAGACGCAGAGTTATGTG GAGACGCAGCAACGACTC
## 193 33026 20440 1067
## GAGACGCAGCATTGATCT GAGACGCATGCACAGAGA GTCCCATCAAAGTGTGTG GTCCCATCCAGAGTCCTT
## 559 33313 198 53498
## GTCCCATCCATTTGCGAT GTCCCATCCCACGTCTAC GTCCCATCGAGTTATGTG GTCCCATCGCAACGACTC
## 198 34929 18678 1115
## GTCCCATCGCATTGATCT GTCCCATCTGCACAGAGA TTCACACCAAAGTGTGTG TTCACACCCAGAGTCCTT
## 551 29867 43 21667
## TTCACACCCATTTGCGAT TTCACACCCCACGTCTAC TTCACACCGAGTTATGTG TTCACACCGCAACGACTC
## 66 11774 5074 5
## TTCACACCGCATTGATCT TTCACACCTGCACAGAGA TTGCCCGTAAAGTGTGTG TTGCCCGTCAGAGTCCTT
## 157 12597 245 64226
## TTGCCCGTCATTTGCGAT TTGCCCGTCCACGTCTAC TTGCCCGTGAGTTATGTG TTGCCCGTGCAACGACTC
## 298 35281 17522 988
## TTGCCCGTGCATTGATCT TTGCCCGTTGCACAGAGA
## 779 43029I am far from saturated sequencing here I think. Top count for MT-RNR2 is 64,226
Barnyard plots
Some molecular swapping but much cleaner than minibulk
Natural scale
plt5 <- ggplot(data=tb, aes(y=subsets_Human_sum, x=subsets_Mouse_sum, colour=Sample)) +
geom_point() +
scale_colour_brewer(type="qualitative", palette = "Dark2") +
xlab("Mouse counts") + ylab("Human Counts")
plt5
Natural scale well label
plt5 <- ggplot(data=tb, aes(y=subsets_Human_sum, x=subsets_Mouse_sum, colour=Sample, label=Buffer)) +
geom_text() +
scale_colour_brewer(type="qualitative", palette = "Dark2") +
xlab("Mouse counts") + ylab("Human Counts")
plt5
Log scale
plt6 <- ggplot(data=tb, aes(y=subsets_Human_sum+1, x=subsets_Mouse_sum+1, shape=Sample, colour=Buffer)) +
geom_point() +
scale_colour_brewer(type="qualitative", palette = "Dark2") +
xlab("Mouse counts") + ylab("Human Counts") +
scale_x_continuous(trans='log10') + scale_y_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "bl")
plt6
Conclusion
2x DNA / RNA shield doesn’t work very well, probably salt concentration. Same for Qiagen buffer RLT
- 1x TCL = cells pelleted and washed once in PBS -> pellet resuspended in 1x TC lysis buffer
- RLT= cells pelleted and washed once in PBS -> pellet resuspended in Qiagen RLT buffer
- 2x TCL = cells in media lysed in equal volume 2x TCL buffer
- DRS = cells in media lysed in equal volume 2x DNA / RNA shield
Session info
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Red Hat Enterprise Linux 9.4 (Plow)
##
## Matrix products: default
## BLAS: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRblas.so
## LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRlapack.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] scales_1.3.0 ggthemes_5.1.0
## [3] here_1.0.1 patchwork_1.3.0
## [5] platetools_0.1.7 scater_1.32.1
## [7] scuttle_1.14.0 lubridate_1.9.3
## [9] forcats_1.0.0 stringr_1.5.1
## [11] dplyr_1.1.4 purrr_1.0.2
## [13] readr_2.1.5 tidyr_1.3.1
## [15] tibble_3.2.1 ggplot2_3.5.1
## [17] tidyverse_2.0.0 SingleCellExperiment_1.26.0
## [19] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [21] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
## [23] IRanges_2.38.1 S4Vectors_0.42.1
## [25] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [27] matrixStats_1.4.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2
## [3] farver_2.1.2 vipor_0.4.7
## [5] viridis_0.6.5 fastmap_1.2.0
## [7] digest_0.6.37 rsvd_1.0.5
## [9] timechange_0.3.0 lifecycle_1.0.4
## [11] magrittr_2.0.3 compiler_4.4.1
## [13] rlang_1.1.4 sass_0.4.9
## [15] tools_4.4.1 utf8_1.2.4
## [17] yaml_2.3.10 knitr_1.48
## [19] labeling_0.4.3 S4Arrays_1.4.1
## [21] DelayedArray_0.30.1 RColorBrewer_1.1-3
## [23] abind_1.4-8 BiocParallel_1.38.0
## [25] withr_3.0.1 fansi_1.0.6
## [27] beachmat_2.20.0 colorspace_2.1-1
## [29] cli_3.6.3 rmarkdown_2.28
## [31] crayon_1.5.3 generics_0.1.3
## [33] rstudioapi_0.17.0 httr_1.4.7
## [35] tzdb_0.4.0 DelayedMatrixStats_1.26.0
## [37] ggbeeswarm_0.7.2 cachem_1.1.0
## [39] zlibbioc_1.50.0 parallel_4.4.1
## [41] XVector_0.44.0 vctrs_0.6.5
## [43] Matrix_1.7-0 jsonlite_1.8.9
## [45] BiocSingular_1.20.0 hms_1.1.3
## [47] BiocNeighbors_1.22.0 ggrepel_0.9.6
## [49] irlba_2.3.5.1 beeswarm_0.4.0
## [51] jquerylib_0.1.4 glue_1.8.0
## [53] codetools_0.2-20 stringi_1.8.4
## [55] gtable_0.3.5 UCSC.utils_1.0.0
## [57] ScaledMatrix_1.12.0 munsell_0.5.1
## [59] pillar_1.9.0 htmltools_0.5.8.1
## [61] GenomeInfoDbData_1.2.12 R6_2.5.1
## [63] sparseMatrixStats_1.16.0 rprojroot_2.0.4
## [65] evaluate_1.0.1 lattice_0.22-6
## [67] highr_0.11 bslib_0.8.0
## [69] Rcpp_1.0.13 gridExtra_2.3
## [71] SparseArray_1.4.8 xfun_0.48
## [73] pkgconfig_2.0.3